标题： 基于囚禁离子量子模拟器的量子近似优化 显示英文标题

标题： Quantum Approximate Optimization with a Trapped-Ion Quantum Simulator

摘要： 量子计算机和模拟器可能比其经典对应物具有显著优势，为量子多体系统提供见解，并可能解决诸如优化和可满足性等指数级困难的问题。 在这里，我们报告了首次使用模拟量子模拟器实现浅深度量子近似优化算法（QAOA），以估计具有可调长程相互作用的横向场伊辛模型的基态能量。 首先，我们全面搜索变分控制参数，以最多40个被捕获离子的量子比特近似基态能量。 然后，我们将量子模拟器与经典算法接口，以更高效地找到使系统能量最小化的最优参数集。 最后，我们通过单次测量和对每个量子比特的高效测量，从QAOA输出的完整概率分布中进行采样。 显示英文摘要

摘要： Quantum computers and simulators may offer significant advantages over their classical counterparts, providing insights into quantum many-body systems and possibly solving exponentially hard problems, such as optimization and satisfiability. Here we report the first implementation of a shallow-depth Quantum Approximate Optimization Algorithm (QAOA) using an analog quantum simulator to estimate the ground state energy of the transverse field Ising model with tunable long-range interactions. First, we exhaustively search the variational control parameters to approximate the ground state energy with up to 40 trapped-ion qubits. We then interface the quantum simulator with a classical algorithm to more efficiently find the optimal set of parameters that minimizes the resulting energy of the system. We finally sample from the full probability distribution of the QAOA output with single-shot and efficient measurements of every qubit.